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join our multi-disciplinary team of researchers, including immunologists, PhD students, physician-scientists, and bioinformaticians. The postdoctoral fellow will work in an NIH-funded laboratory and will
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well as opportunities for conference attendance and collaborations with other research organisations. Key Skills, Experience and Qualifications PhD in machine learning, computer vision or a related field. Established
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findings to military stakeholders and the implementation of evidence-based practices within military medical populations. Therefore, applicants should have experience working with, or significant interest in
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD degree (or foreign equivalent) in Electrical Engineering or a closely related field in hand by
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to effective therapeutic strategies targeting IDPs Collaborate on the development of open-source machine learning tools to support these therapeutic designs Work closely with high-throughput screening teams
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probabilistic machine learning and geospatial sciences. Limited teaching may be arranged, if mutually agreed, in exchange for a contract extension. Qualification Requirements Applicants must hold a PhD degree in
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some